1990results about "Other databases clustering/classification" patented technology

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

Providing generative artificial intelligence (AI)-enabled notebook interfaces for a security framework, including: receiving a request to generate a notebook interface for a security framework monitoring a cloud deployment; generating, in response to the request, the notebook interface, wherein the notebook interface comprises one or more notebook cells for interacting with the security framework, wherein the one or more notebook cells comprise a natural language input cell for querying a generative artificial intelligence (AI) model; and presenting the notebook interface.

The invention discloses a robot interactive question-answering method and system based on a large service model, and belongs to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction and fusion of the original input of a user, obtaining a multi-modal vector, building a recognition model, outputting an emotion recognition tag, an intention recognition tag, a user state vector and a session history, fusing the user state vector with a domain knowledge graph, outputting a structure reasoning vector and a joint condition vector, and splicing a knowledge abstract vector with a condition fusion vector to obtain the user state vector as the input of a text generator; and outputting a natural language answer and a user state vector, mapping the natural language answer into a multi-modal behavior action, and finally outputting a voice answer signal and the multi-modal behavior action. According to the method, the answer strategy of the robot is dynamically adjusted according to the emotional state of the user by combining context awareness and knowledge reasoning technologies, so that intelligent interaction of emotional resonance and professional depth coexistence is realized.

The invention relates to the field of enterprise industry chain evaluation, and discloses an artificial intelligence-based enterprise industry chain multi-dimensional evaluation system and method, and the system comprises a data collection module which is used for obtaining industry chain data from a structured database, an unstructured text and a third-party platform; the data fusion module is used for constructing a dynamically updated industrial chain knowledge graph through a natural language processing technology; the multi-dimensional evaluation module comprises a financial health evaluation sub-module, a supply chain toughness evaluation sub-module, an innovation power evaluation sub-module and a carbon emission evaluation sub-module, and each sub-module generates quantitative scores by adopting an LSTM neural network, a PageRank algorithm and a space-time diagram convolutional network model and weights the quantitative scores to obtain comprehensive scores; the risk early warning module is used for simulating a triple conduction effect of an external event on an industrial chain based on a graph neural network and generating a risk index and a coping strategy; and the visual interaction interface dynamically displays the evaluation result. According to the method, the problems of data splitting and poor dynamic adaptability in a traditional evaluation method are solved, and accurate evaluation of the industrial chain is realized.

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The invention discloses a knowledge model data management system based on artificial intelligence, and relates to the field of data management, and the system comprises the following components: a data collection and storage module, an intelligent prediction and analysis module, an abnormal comprehensive processing module, a knowledge graph and traceability module and a man-machine interaction management module. According to the invention, the intelligent prediction analysis module is combined with a time sequence prediction model and a causal reasoning algorithm, abnormal fluctuation and causal association thereof in data can be accurately judged, the accuracy of anomaly detection is effectively improved, and meanwhile, the knowledge graph and traceability module uses the constructed knowledge graph and reinforcement learning algorithm to improve the accuracy of anomaly detection. According to the method, the relation chain can be quickly traced from the abnormal data, the problem source can be positioned, the abnormal traceability efficiency is remarkably improved, and the functions act together, so that the system can more quickly and accurately discover and process problems when facing a complex data environment, and the stability and reliability of data management are guaranteed.

The invention relates to the technical field of artificial intelligence, and provides a large model cue word construction method and device, equipment, a medium and a program product, and the method comprises the steps: extracting query content and a business scene identifier from a user request; based on the business scene identifier, matching a target cue word template from a cue word template database; and based on the intention category identifier associated with the query content and the target cue word template, constructing a large model cue word. According to the large model cue word construction method provided by the invention, the target cue word template is matched from the cue word template database based on the business scene identifier, and the large model cue word is constructed in combination with the intention category identifier associated with the query content, so that the adaptive large model cue word can be dynamically constructed according to the real-time user intention and the business scene, and the user experience is improved. The method has high flexibility, and meanwhile, through scene adaptation and intention accurate guidance, the prompt words can be constructed in a unified and dynamic manner by multiple services, and the expansibility and adaptability of service processing are improved.

The invention provides a question and answer method, device and equipment based on a multi-dimensional knowledge base, a medium and a product, and the method comprises the steps: recognizing an intention in an original question, and obtaining a plurality of intention categories; according to the intention category, disassembling the original question into sub-query questions; according to the dimension labels carried by the sub-query questions, querying in the corresponding knowledge database to obtain a retrieval result set; and inputting the retrieval result set into the large language model to obtain an answer which is output by the large language model and aims at the original question. According to the method, the retrieval results of the different sub-query questions are obtained through retrieval in the preset knowledge databases of the different subdivision fields, and the final answers are generated according to the retrieval results; the defect that the retrieval result is inaccurate due to the fact that knowledge feature values in a traditional unified database are prone to being lost or knowledge of different dimensions is prone to being confused during retrieval is overcome, and more comprehensive and accurate answers can be obtained due to the fact that sub-query questions in multiple dimension fields and most relevant background knowledge are included.

Automated deployment of an anomaly detection framework, including: receiving data describing a deployment of an anomaly detection framework in a cloud computing environment; generating, based on the data, a bundle of configuration resources for deploying the anomaly detection framework in the cloud computing environment; and provide, in response to receiving the data, a reference to the bundle.

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.). This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model and an LLM to automatically determine the relevant classification label(s) for an unlabelled data item.

Disclosed in the embodiments of the present disclosure are a knowledge graph construction method and apparatus, a storage medium and a terminal. The method comprises: generating at least one user entity on the basis of user data in a target scenario, and generating at least one data entity on the basis of unstructured data in the target scenario; for the data entities of each type, separately extracting a data association relationship for every two data entities of the type; and on the basis of a corresponding relationship between each user entity and each data entity and the data association relationships between the data entities, constructing a knowledge graph corresponding to the target scenario. The present application generates the user entity and the data entities of the unstructured data on the basis of original data in scenarios; and by means of analysis of the unstructured data, the present application extracts the association relationship between any two entities among data entities of each type, and uses same as edges for connecting entity nodes, thus effectively integrating data of various types in the scenarios, and constructing knowledge graphs with richer and more comprehensive knowledge in the target scenarios.

Methods, systems, and non-transitory computer readable storage media are disclosed for optimizing computer memory usage for lookup lists in computer memory via probabilistic data structures. For example, the disclosed system generates a probabilistic data structure (e.g., a Bloom filter) to represent data in a lookup list including multi-token items by hashing items of the lookup list to sets of bit values in a bit vector. The disclosed system classifies text content in a digital document by utilizing a maximum number of tokens from multi-token items in the lookup list to select and compare sets of sequential tokens in the digital document to the probabilistic data structure. The disclosed system also iteratively reduces the number of tokens in sets of sequential tokens for subsequent comparisons. Furthermore, in some aspects, the disclosed system causes a computing device to modify a digital document and / or database operations based on the classifications.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine-learning models to classify content items and automatically organize the content items within a file structure according to their content item classifications. For instance, a content item classification system generates one or more content item classification models to determine classifications for content items and / or folders. In some instances, the classification system detects when new content items are added to a smart folder, determines destination folders to which the content items belong based on classifying the content items, and automatically moves the content items accordingly. In various instances, the classification system generates and utilizes a classification model to organize content items into dynamically-generated folders. In example implementations, the classification system generates and utilizes a classification model to automatically organize existing content items into existing folders.

Technology is disclosed for programmatically generate answers for a user that are responsive to aspects of a conversation, which may be occurring in near real-time. In one implementation, a conversation record is processed to determine a conversation representation and corresponding representation embedding. The representation embedding is used to determine a set of relevant passages of documents within a knowledge base. An answer-generation input instruction for a language model is generated based on the conversation representation, aspects of the relevant passages, and an answer-format instruction. The language model is directed to produce an answer output that includes accessible, passage-level citations for each portion of the answer derived from a particular passage the knowledge base, enabling a user to directly access the relevant passage. The generated answer, along with the citations, is presented via a user interface, thereby improving the efficiency and quality of user assistance.

An illustrative method includes identifying, based on a scan of a compute environment associated with an entity, a plurality of attack paths from one or more networks to one or more datasets associated with the entity, determining individual risk scores for each attack path indicating a level that each attack path could be exploited to access the one or more datasets, and determining, based on the individual risk scores, an absolute risk score for the entity indicating a status of a security posture for the entity. A risk mitigation operation associated with the entity is performed based on the absolute risk score.

The invention discloses an urban reserve land intelligent classification method and system based on spatial data fusion, and relates to the technical field of data processing, and the method comprises the steps: carrying out the formalized expression of a land use rule of a preset urban region, and constructing a land domain knowledge graph; collecting multi-source spatial data, performing feature extraction and alignment, and generating a high-dimensional feature vector of each plot; and performing hybrid classification evaluation based on the high-dimensional feature vector of each land parcel, synchronously outputting land use types, development potential levels and land reference value intervals of the land parcels, and calling a land domain knowledge graph to perform compliance verification and correction. The technical problems that an existing urban reserve land classification method depends on artificial experience and lacks accurate and multi-dimensional data fusion and compliance check, so that the accuracy and practicability of a classification result are low are solved, and the purpose that the classification accuracy and practicability of the urban reserve land are improved through multi-source spatial data fusion and an intelligent classification framework is achieved. And the accuracy and practicability of urban reserve land classification are improved.

The invention provides a DMP-based crowd selection method and apparatus, and an electronic device, a data management platform pre-processes multi-source user data, generates user features corresponding to the pre-processed data based on a preset rule, converts the obtained user features into user tags based on a pre-constructed tag system, and sends the user tags to the user management platform; the method comprises the following steps: acquiring a user tag, storing the acquired user tag in a distributed storage mode, displaying the stored user tag through an operation interface, converting a circling operation for a target user tag displayed on the operation interface into a rule tree, and circling a target user corresponding to the target user tag based on the rule tree. And associating the circling result with the identification information of the target user corresponding to the circling result to generate a crowd packet, and expanding the obtained crowd packet by adopting a similar crowd expansion algorithm. According to the invention, the problems of complex rule configuration, insufficient real-time performance, limited expansibility and the like of a DMP-based crowd selection system in the prior art can be solved.

The invention discloses a federal large model adaptive low-rank fine tuning method and device, computer equipment and a storage medium. The method comprises the following steps: decomposing a low-rank adaptive matrix of a large language model into a server shared matrix and a client private matrix; initializing a private matrix and an adaptive diagonal matrix; the client dynamically trims the low-contribution dimension and adjusts the actual effective rank according to the standard deviation of each element in the adaptive diagonal matrix, and combines the shared matrix and the private matrix to obtain an updated private matrix; the server performs aggregation through a rank-aware aggregation strategy, and performs fine adjustment on the shared matrix by using the public data set to obtain a global model; and deploying the global model to a server and each client. By implementing the method, adaptive fine tuning of the large language model can be efficiently realized, meanwhile, the problems of aggregation interference, low resource utilization rate and insufficient generalization ability in a traditional method are solved, and the technical scheme can be applied to the fields of finance and medical health.

A processor-implemented method including separating a first document into a second document, the second document including a first metadata portion of first metadata of the first document, and a third document, the third document including a first content portion of content of the first document, classifying the second document and the third document into a first material set and a second material set, respectively, and indexing the second document and the third document according to a correlation of the second document and the third document.

The following relates generally to using generative AI to: (i) classify documents; (ii) generate prompts to classify documents; (iii) evaluate the classification performance of prompts; (iv) generate updates to prompts; and / or (v) train classifiers. In some embodiments, one or more processors: generate a prompt for input to the generative AI model; generate classifications for a set of documents from the corpus of documents by inputting the set of documents and the prompt to the generative AI model; based on the classifications, provide the set of documents to a review platform for manual review by a reviewer; obtain review data associated with a subset of documents from the set of documents; and train, by executing a training algorithm, a classifier using the review data as ground truth data, wherein the training algorithm is configured to analyze extracted relevant document portions of the subset of documents to train the classifier.

A computer-implemented method for classifying points in a point cloud obtained by an active detection and ranging system is provided. The method comprises: computing a latent representation of the point cloud, determining from the latent representation, for each point in the point cloud, a probability that the point represents an object belonging to one or more object classes, and determining from the latent representation, for each point in the point cloud, a probability the point represents a ghost object. An object classification network is used for determining the probability that the point represents an object, and a ghost classification network is used for determining the probability the point represents a ghost object.

A context-aware intelligent retrieval method suitable for multi-domain heterogeneous data relates to the technical field of intelligent retrieval and big data processing, combines context-aware domain semantic enhancement with a cross-domain semantic hash retrieval model, and realizes effective alignment between a source domain and a target domain in a unified feature space. A domain sharing encoder and a domain special encoder are constructed, and a residual fusion strategy is adopted to integrate domain sharing features and domain special features, so that the relation between image detail information and contexts is reserved, and cross-domain feature migration is realized. In the training process, effective modeling of domain specific information and domain shared information is ensured by utilizing a domain classification loss and distribution alignment strategy.

The invention provides a text classification method and a deep learning model training method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of deep learning, natural language processing, content security and intelligent search. According to the specific implementation scheme, the method comprises the steps of generating semantic features of an input text by using a shared base network; according to the semantic features and a splicing weight matrix of the plurality of tower networks, determining respective classification results of a plurality of text classification tasks respectively corresponding to the plurality of tower networks, the splicing weight matrix being obtained by splicing weight matrixes of the same processing layer of the plurality of tower networks; and according to respective classification results of the plurality of text classification tasks, determining a target classification result of the input text.

The invention discloses a simulation data management method based on a servitization integrated architecture, and relates to the technical field of combat simulation and deduction, and the method comprises the steps: building a servitization integrated data management architecture according to a data collection layer, a data storage layer, a data management layer, a data management layer and a data service layer; based on a service integrated data management architecture, data standardization is carried out, a data model is designed, data fusion preprocessing, data integration and data analysis are carried out on collected multi-source heterogeneous data according to a data standard and the data model, data fusion and warehousing management are completed, and meanwhile balance and unification of data security and sharing are guaranteed. According to the invention, a one-stop and all-around data management solution can be provided for simulation application.

The invention discloses a facility vegetable tomato leaf miner pest comprehensive prevention and control system, and relates to the technical field of facility agriculture pest prevention and control, and the system comprises a space distribution obtaining module which obtains the space distribution data of a pest invasion path in a facility agriculture environment, and determines the protection level demand intensity of different regions; the protection parameter calculation module is used for calculating specification parameters and installation density of insect prevention grids in all areas according to the protection level requirement strength, and if the pest density of the peripheral area exceeds a preset threshold value, the aperture of the insect prevention grids is adjusted to the minimum specification, and a first-layer electric control isolation net protection barrier configuration scheme is obtained; the comprehensive control system for the greenhouse vegetable tomato leaf miner pests can effectively deal with the complexity and variability of pest invasion, and the pest control effect and sustainability of the greenhouse agricultural environment are remarkably improved.

The invention relates to the technical field of collaborative management, in particular to a business collaboration method based on a process engine, which comprises the following steps: acquiring approval process nodes and post fields, establishing node parameter mapping, extracting customer reconciliation process field paths, generating a field coverage set, analyzing contract archiving responsibility actions, and constructing a responsibility sequence link. And matching a financial reimbursement process cooperation path, correcting an offset node to generate a cooperation task flow, calculating an internal instruction process node priority, and generating a task triggering list. According to the method, the node fields and the role identifiers in the approval process are obtained, the field consistency mapping relation is constructed, and a field continuous path screening mechanism is combined, so that the penetrating power and the tracing accuracy of parameters among cross-process nodes are improved, and the sequence of node responsibilities has a verifiable logic chain; through a node offset detection and path connectivity construction mode, accurate task execution sequence control is provided in multi-source task scheduling.

A conversation content generation method and apparatus, a storage medium and a terminal are provided. The method includes: acquiring a current utterance entered by a user; reading a preset topic transfer graph and target topic, wherein the topic transfer graph includes nodes and connecting lines between the nodes, the nodes correspond to topics in one-to-one correspondence, each connecting line points from a first node to a second node, a weight of the connecting line indicates probability of transferring from a topic corresponding to the first node to a topic corresponding to the second node, and the topic transfer graph includes a node corresponding to the target topic; determining a topic of reply content of the current utterance at least based on the current utterance, the topic transfer graph and the target topic, and recording it as a reply topic; generating the reply content at least based on the reply topic.

The invention discloses a six-dimensional judgment intelligent operation and maintenance diagnosis method and system for an oil field gathering and transportation station yard, and belongs to the technical field of oil field gathering and transportation intelligent operation and maintenance. Comprising a data acquisition and single point location model establishment module, a six-dimensional analysis and judgment module, a six-dimensional fusion processing module and a feedback updating module, comprising flow, pressure, temperature, liquid level, water content, pump frequency / current, valve position / state position, furnace gas amount / air door and station control alarm position data in an oil field gathering and transportation station. According to the six-dimensional judgment intelligent operation and maintenance diagnosis method and system for the oil field gathering and transportation station, by integrating multi-link analysis, dynamic threshold adjustment, a closed-loop optimization mechanism and safety guarantee design, the problems that current diagnosis is poor in stability, insufficient in precision, weak in decision support and the like are solved; and reliable technical support is provided for efficient and stable operation of an oil field gathering and transportation station.

The invention relates to the technical field of intelligent power grids, in particular to a BIM-fused power transmission line three-dimensional parametric modeling method and a topological optimization system. Comprising the steps that basic data of a power transmission line are acquired, and the basic data comprise line path planning data, tower design parameters, lead and lightning conductor specification parameters, geographic information data and meteorological data; creating a basic model framework of the power transmission line based on the BIM platform, importing the line path planning data into the BIM platform, determining the overall trend of the power transmission line, and arranging a tower model on the path according to the tower design parameters; conducting parametric modeling on the wire and the lightning conductor according to specification parameters of the wire and the lightning conductor; according to the method, deep fusion of the model and an actual scene can be realized, and power transmission line modeling with dynamic simulation analysis capability is realized.

The invention discloses an intelligent rehabilitation bed body dynamic supporting system based on pressure feedback, and relates to the technical field of intelligent rehabilitation bed body dynamic supporting, and the intelligent rehabilitation bed body dynamic supporting system comprises a sensing acquisition module, a data fusion module, an individual modeling module, a strategy generation module, an execution driving module, a state feedback module and a remote interaction module; the sensing and collecting module is used for collecting pressure, temperature, humidity and heat distribution information of a contact area of a bedridden patient and a bed body in real time in a distributed multi-mode sensing mode and outputting original sensing signals. The data fusion module is used for performing multi-source data fusion processing on the original sensing signals from the sensing acquisition module by adopting a Kalman filtering algorithm, extracting comprehensive health indexes reflecting the microcirculation state of local tissues, and outputting fused physiological evaluation data; and the individual modeling module is used for training the body type, the weight, the medical history information and the historical pressure distribution data of the patient by adopting a machine learning model.